brand.ratings <- read.csv("http://goo.gl/IQl8nc")
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18 January 2022
brand.ratings <- read.csv("http://goo.gl/IQl8nc")
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head(brand.ratings)
## perform leader latest fun serious bargain value trendy rebuy brand ## 1 2 4 8 8 2 9 7 4 6 a ## 2 1 1 4 7 1 1 1 2 2 a ## 3 2 3 5 9 2 9 5 1 6 a ## 4 1 6 10 8 3 4 5 2 1 a ## 5 1 1 5 8 1 9 9 1 1 a ## 6 2 8 9 5 3 8 7 1 2 a
summary(brand.ratings)
## perform leader latest fun ## Min. : 1.000 Min. : 1.000 Min. : 1.000 Min. : 1.000 ## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 4.000 1st Qu.: 4.000 ## Median : 4.000 Median : 4.000 Median : 7.000 Median : 6.000 ## Mean : 4.488 Mean : 4.417 Mean : 6.195 Mean : 6.068 ## 3rd Qu.: 7.000 3rd Qu.: 6.000 3rd Qu.: 9.000 3rd Qu.: 8.000 ## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000 ## serious bargain value trendy ## Min. : 1.000 Min. : 1.000 Min. : 1.000 Min. : 1.00 ## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 3.00 ## Median : 4.000 Median : 4.000 Median : 4.000 Median : 5.00 ## Mean : 4.323 Mean : 4.259 Mean : 4.337 Mean : 5.22 ## 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 7.00 ## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.00 ## rebuy brand ## Min. : 1.000 Length:1000 ## 1st Qu.: 1.000 Class :character ## Median : 3.000 Mode :character ## Mean : 3.727 ## 3rd Qu.: 5.000 ## Max. :10.000
str(brand.ratings)
## 'data.frame': 1000 obs. of 10 variables: ## $ perform: int 2 1 2 1 1 2 1 2 2 3 ... ## $ leader : int 4 1 3 6 1 8 1 1 1 1 ... ## $ latest : int 8 4 5 10 5 9 5 7 8 9 ... ## $ fun : int 8 7 9 8 8 5 7 5 10 8 ... ## $ serious: int 2 1 2 3 1 3 1 2 1 1 ... ## $ bargain: int 9 1 9 4 9 8 5 8 7 3 ... ## $ value : int 7 1 5 5 9 7 1 7 7 3 ... ## $ trendy : int 4 2 1 2 1 1 1 7 5 4 ... ## $ rebuy : int 6 2 6 1 1 2 1 1 1 1 ... ## $ brand : chr "a" "a" "a" "a" ...
cor(brand.ratings.means[, 2:10])
## perform leader latest fun serious ## perform 1.000000000 0.66073151 -0.258730125 -0.7640855 0.629973310 ## leader 0.660731514 1.00000000 0.012264047 -0.8094976 0.951151730 ## latest -0.258730125 0.01226405 1.000000000 0.3188666 0.008612981 ## fun -0.764085534 -0.80949757 0.318866591 1.0000000 -0.743040353 ## serious 0.629973310 0.95115173 0.008612981 -0.7430404 1.000000000 ## bargain 0.127264450 0.08202056 -0.620974023 -0.2018282 -0.004474103 ## value 0.208052640 0.17399660 -0.705973921 -0.3603228 0.065190457 ## trendy -0.009716477 0.13646220 0.831557451 0.1610722 0.227623914 ## rebuy 0.550162549 0.38481684 -0.755749762 -0.5496990 0.324762755 ## bargain value trendy rebuy ## perform 0.127264450 0.20805264 -0.009716477 0.5501625 ## leader 0.082020563 0.17399660 0.136462199 0.3848168 ## latest -0.620974023 -0.70597392 0.831557451 -0.7557498 ## fun -0.201828159 -0.36032280 0.161072218 -0.5496990 ## serious -0.004474103 0.06519046 0.227623914 0.3247628 ## bargain 1.000000000 0.95555229 -0.760809215 0.7454187 ## value 0.955552292 1.00000000 -0.819903233 0.7846262 ## trendy -0.760809215 -0.81990323 1.000000000 -0.5418059 ## rebuy 0.745418712 0.78462624 -0.541805889 1.0000000
corrplot(cor(brand.ratings.means[, 2:10]), order="hclust")